Nominal Data Research Articles

Ostomy Care Nurses' Knowledge and Practice Related to Prevention and Management of Parastomal Hernias in Adults: A Nationwide Survey of UK Stoma Care Nurses.

The purpose of this study was to describe the knowledge and practices of ostomy care nurses related to the United Kingdom's Association of Stoma Care Nurses (ASCN) 2016 guidelines for prevention and management of parastomal hernia (PSH) in adults. Cross-sectional descriptive study. The target population was approximately 300 stoma care nurses; 120 useable responses to the survey were received, reflecting a response rate of approximately 40%. A 31-item questionnaire was developed for the purposes of this study, consisting of multiple choice and short answer questions grouped into 2 sections; demographics, and knowledge and practices. The questionnaire was distributed to members of the ASCN who work with adults via an online survey platform with the aim of reaching as many respondents as possible. The Chi-square test was used to determine relationships between the nominal demographic data and the nominal knowledge and practices data. Almost two-thirds of respondents (65%, n =78) rated their knowledge relating to prevention and management of PSH as good or excellent. Nevertheless, 79.8% (n=95) indicated they needed additional education to improve their knowledge and practice related to prevention of PSH. Those who rated their knowledge as excellent or good were significantly more likely to be aware of the guidelines compared to those who ranked their knowledge as average or poor. Study findings suggest variability in knowledge among UK stoma care nurses, along with a desire for additional education in this area of care.

Analisis Penghitungan Anggaran Belanja Daerah Badan Perencanaan Pembangunan Daerah (BAPPEDA) Kota Medan Menggunakan Metode Activity Based Costing (ABC)

The Regional Revenue and Expenditure Budget (APBD), is the annual financial plan of regional governments in Indonesia which is approved by the Regional People's Representative Council. The APBD is determined by Regional Regulations and the APBD Fiscal Year covers a period of one year, starting from January 1 to December 31. This research aims to determine the grouping of activities from highest to lowest using the Activity Based Costing (ABC) method calculation. The research results show two analyzes, namely analysis in the sub-activities section and analysis in the operations expenditure section. The data used is Quantitative Data, which is data in the form of numbers or nominal data, data in the form of the Medan City BAPPEDA Budget Realization Report . Qualitative data is data that is not in the form of numbers or nominal, data in the form of direct observations from BAPPEDA Medan City regarding the implementation of the APBD. Data obtained from PERWAL APBD 2022 BAPPEDA Section is the total budget amount of Rp. 27,307,689,269 with 3 general activities, 12 sub-activities and 48 total operational expenditures.

IMPLEMENTATION OF AES AND SHA-3 CRYPTOGRAPHY ALGORITHMS IN SECURING USER SENSITIVE DATA ON THE ARTESIAN WATER BILL PAYMENT TRANSACTION WEBSITE

Transacting online is something that is familiar to almost everyone in the digital era. The presence of digital transactions brings convenience in carrying out trading activities, but the use of the internet in creating easy transactions with several challenges in the form of hacking. In order to realize secure transactions, the level of protection on digital transaction service provider sites must be made as strong as possible to prevent digital attacks. Therefore, a protection system for user payment data on one of the transaction websites was created by applying the AES and SHA-3 cryptographic algorithm methods. The AES algorithm method was chosen because its implementation is not complicated but has proven its security, while SHA-3 is used to create unique keys from user passwords with the latest hash technology, so that the data encryption and decryption process becomes more protected because it uses the secret key of each user but not directly. The application of cryptographic algorithms will provide additional protection for the security of nominal user transaction data from cyber attacks so that the data. This research will produce a cryptography program flow with the Python programming language and examples of encryption and hash process results during program execution.

Clustering Mixed-Type Data via Dirichlet Process Mixture Model with Cluster-Specific Covariance Matrices

Many studies have shown successful applications of the Dirichlet process mixture model (DPMM) for clustering continuous data. Beyond continuous data, in practice, one can expect to see different data types, including ordinal and nominal data. Existing DPMMs for clustering mixed-type data assume a strict covariance matrix structure, resulting in an overfit model. This article explores a DPMM for mixed-type data that allows the covariance matrix to differ from one cluster to another. We assume an underlying latent variable framework for ordinal and nominal data, which is then modeled jointly with the continuous data. The identifiability issue on the covariance matrix poses computational challenges, thus requiring a nonstandard inferential algorithm. The applicability and flexibility of the proposed model are illustrated through simulation examples and real data applications.

Sex differences of 12 lead electrocardiographic findings of the rugby football league athlete

Cardiac electrical adaptations occur within athlete populations. Sex-related variations in the normal athlete electrocardiogram (ECG) have been observed, with males exhibiting increased QRS and PR intervals, while females have demonstrated increased QTc intervals in university-level athletes. Male athletes also maintain greater weekly training hours compared to female athletes, which is associated with increased ECG changes. The presence of these differences within Rugby Football League (RFL) athletes is unknown. This study aimed to investigate any sex-related variations in the 12-lead ECG in RFL athletes. Fifty nine RFL athletes (32 males [age = 25 ± 5 years] and 27 females [age = 24 ± 5 years]) were included in the study. One female was excluded with inferior T wave inversion and was referred for further investigation. The 12-lead ECG was collected as part of the athlete’s pre-season cardiac screening alongside completion of a health questionnaire, assessment of height and weight and brachial artery pressure. Continuous variables (i.e. heart rate, PR interval, QRS interval etc.), were analysed using an independent t-test or a Mann-Whitney U test as appropriate. For assessment of nominal data (i.e. incidence of early repolarisation and junctional rhythm etc.) a chi-squared test or Fishers exact test were used. A Spearman’s correlation coefficient was used to determine any association between training hours and ECG findings. Ethics approval was obtained from Liverpool John Moores University Ethics Committee. Height (183 ± 5 vs. 169 ± 5 cm, P <0.001), weight (95 ± 11 vs. 77 ± 11 kg, P <0.001) and weekly training hours (18 ± 5 vs. 12 ± 5 hrs, P = 0.001) were higher in male athletes. PR interval (156 ± 34 vs. 142 ± 19 ms, P = 0.002), QRS duration (98 ± 9 vs. 88 ± 7 ms, P <0.001) and incidence of early repolarisation (47% vs. 11%, P = 0.004) were greater in males. Heart rate (57 ± 9 vs. 63 ± 11 bpm P = 0.016) and QTc interval (391 ± 20 vs. 414 ± 17 ms, P <0.001) were greater in females. There was a significant negative correlation between weekly training hours and QTc interval (rs = −0.355, P = 0.009) only. In conclusion, there are significant sex-related ECG differences within RFL athletes that partially mirror previous research. These findings emphasise the importance of considering sex-related variations, during cardiac screening on RFL athletes and enhance our understanding of the normal RFL athlete ECG.

SMART quality control analysis of pavement condition data for pavement management applications

Assuring the accuracy and reliability of pavement condition data is crucial for effective decision-making in pavement management. Despite existing data collection protocols, concerns persist regarding data quality. This paper introduces SMART, a systematic statistical method designed to analyze the quality of pavement condition data from field surveys for pavement management applications. SMART employs a structured procedure that expands traditional descriptive statistics by applying interrater reliability statistics combined with bootstrapping methods and Modified Blant Altman diagrams to evaluate data quality. A comparative analysis of interrater statistics, including Cohen’s Kappa (CK), Interclass Correlation (IC), Krippendorff’s Alpha (KA), Percent Agreement (PA), and Weighted Cohen’s Kappa (WCK), is conducted in the research study. As a result, the adoption of KA and Modified Bland-Altman diagrams for data analysis is recommended. KA demonstrates versatility across diverse data types, accommodating nominal, ordinal, interval, and ratio-level data, while Modified Bland-Altman diagrams facilitate data dispersion analysis to visualize possible bias trends for the condition ratings. A case study is presented to demonstrate the applicability of SMART to analyzing Pavement Condition Index (PCI) data provided by the Metropolitan Transportation Commission (MTC) in California. This methodological approach aims to enhance pavement management decisions by ensuring the reliability of condition field survey data through the implementation of robust analytical quality control procedures.

Global disparities in immunotherapy clinical trials: A comprehensive analysis of low- and middle-income countries over the past decade.

1606 Background: Immunotherapy (IO) has largely impacted cancer treatment over the last decade, yet its accessibility and representation in clinical trials remain severely limited in low- and middle- income countries. We investigated the involvement of these countries in IO cancer trials, analyzing country-specific rates and influencing factors. Methods: Data was obtained from clinicaltrials.gov. Advanced search focused on cancer interventional studies from 12/31/2013 to 01/01/2024 with specific IO treatments. Only completed trials were included. Studies unrelated to immunotherapy or cancer were excluded. Country trial rates were calculated per 100k population. Statistical analysis used Chi-square test for nominal data. Results: Of 1593 trials, 1282 were included in the final analysis. Of world’s 217 countries and regions (World Bank) 72 (33.2%) participated in IO cancer trials in the last decade. Zero country from low-income group was involved in the trials. Only 2.4% (31) of all trials, included lower-middle-income countries (LMICs). Out of 54 LMICs, only 8 were represented: Ukraine, Philippines, India, Guatemala, Egypt, Vietnam, Morocco, and Eswatini (Swaziland). Ukraine led this group with 16 trials, while the remaining 7 were included in less than 10 trials each. Upper-middle-income countries (UMICs) were included in 21.3% (273) of trials. Out of 54 UMICs, 22 were involved in the trials; China led this group (174), followed by Russia (69), Brazil (52), Mexico and Turkey (41 each). Among the top 30 countries with the highest trial participation, none included LMICs, and only 5 were UMICs. Of 81 high-income countries (HICs) 42 participated in IO trials. USA was involved in over 65% of all trials (840), followed by Spain (222), France (193), and Canada (180). However, population-adjusted rates varied, with European HICs like Latvia (2.16), Belgium (1.09), Norway (0.87) among 25 other countries, showing higher rates than USA (0.25). Majority of trials done solely in one country were also from USA (65%), and China (79%). A significant link was found between the economic status of participating countries and the funding source of trials (p<0.00001). Industry funded 28 of 31 (90%) trials in LMICs, indicating a reliance on industry funding in these regions. Industry favored adult-only over pediatric-inclusive trials (p<0.0001). Out of 80 trials (6.24%) including children, 22 were industry-funded. Exclusively pediatric IO trials were only 4 (0.3%) of all IO trials. LMICs participated in 3.75% (3) of all pediatric-inclusive trials. Conclusions: Countries with low economic status and children with cancer from both low- and high-income areas are largely underrepresented in IO cancer trials globally. More inclusive IO trials across age groups and LMICs are vital to implement trial results in real-world settings and close the care gap.

PHYSICS TEACHERS’ EPISTEMOLOGICAL BELIEFS: CORE AND PERIPHERAL DIMENSIONS

This study aims to examine physics teachers’ beliefs about knowledge, science, teaching and learning science to understand the relationship among four belief systems. Participants were 59 high school physics teachers, who were interviewed once through a semi-structured interview protocol. The interviews were transcribed and analyzed through constant comparative method to categorize teachers’ beliefs in different levels: traditional, transitional, and constructivist and quantify the nominal data for descriptive, correlational statistics, and structural equation modeling. The results indicated that teachers mostly held transitional beliefs about knowledge and science while their beliefs were constructivist on teaching and learning science. There was a significant relationship between beliefs about knowledge and science as core beliefs and between beliefs about teaching and learning science as peripheral beliefs, positive correlation was found between core and peripheral beliefs. These findings offer a report as a guide to address teachers’ beliefs through the design of future professional development programs.

Factors that influence field hockey footwear selection: An online survey.

Little is known about factors that influence footwear selection by field hockey players. An online survey was used to collect data on participant demographics and physical characteristics, factors influencing footwear selection, perceptions regarding footwear design features on injury and performance, and experiences regarding usability. Nominal and ordinal data were described as absolute frequencies and relative frequencies. Free text responses were analysed using content analysis. Sex-related differences in quantitative and qualitative data were explored. A total of 401 hockey players completed the survey. Participants reported that fit, comfort, support, and cushioning were the most important factors when selecting hockey footwear. Most hockey players believed that stud design could influence athletic performance (65%) and injury risk (63%) but reported having no preference on outsole design or stud shape. Most participants (63%) used hockey-specific footwear, but 46% of female hockey players did not, with 40% using trail running footwear instead. Qualitative analysis revealed that hockey players, particularly female participants, encounter difficulties finding properly fitting footwear. They desire more options for wide or narrow feet and face challenges in accessing suitable hockey shoes due to limited choices and availability. With over a third of field hockey players not using hockey-specific footwear, future research should attempt to understand the reasons and assess the impact of different footwear features on comfort, performance, injury risk, and usability.

The Democratization of Long-Distance Migration: Trajectories and Flows during the “Mobility Transition,” 1850–1910

Abstract This article analyzes and demonstrates the declining social selectivity of migration distance in Europe’s long nineteenth century and argues that this drove a radically new process of democratization of long-distance migration. It uses innovative spatial and quantitative analysis of nominal data on more than 5,000 international migrants who moved to the booming port city of Antwerp in present-day Belgium between 1850 and 1910. Examining the changes in migrants’ origins and trajectories on the one hand, and in their profiles in terms of gender and occupations on the other hand, it argues that the main evolutions observed represent an overall loosening of the ancien régime link between migration distance on the one hand and social selectivity on the other hand. By focusing on gender and social class as markers of social selectivity and by mapping the impressive expansion of the trajectories of Antwerp’s growing number of long-distance migrants, it lays bare the spatial, gender, and social dimensions that contributed to a general process of democratization of long-distance migration. As such, it sheds new light on the dynamics of Europe’s so-called “mobility transition” in the long nineteenth century.

Clinical Manifestations of Dengue in Children and Adults in a Hyperendemic Region of Colombia.

Dengue is the most common arboviral disease in the world. Traditionally, it has affected more adults, but the incidence in children has increased in recent years. Colombia is no stranger to this change; therefore, we aimed to find the differences in signs, symptoms, and clinical, hematological, and hemogram characteristics between children under 12 years old and individuals aged 12 years and older in an endemic region of Colombia in 2020-2022. The analyses were conducted with baseline data, corresponding to a cross-sectional design. Multiple correspondence analysis was used for general, dermatological, and clinical symptom profiles. Discriminant analysis was used for laboratory profiles. Multiple correspondence analysis was applied to nominal categorical data, employing Euclidean distances to analyze age groups. Discriminant analysis was applied to a training sample and validated on a test sample. The overall agreement of the model's discrimination, sensitivity, specificity, and fit indicators was calculated. The results indicated that individuals under 12 years exhibited distinct dermatological and clinical features, including rash, pruritus, hypotension, lymphocyte count, and platelet count, compared with those aged 12 years and older. In contrast, those 12 years and older were profiled for general and clinical symptoms such as pain (back pain, retro-orbital pain, headache), dizziness, chills, hematuria, tachypnea, and elevated/high hematocrit, hemoglobin, and basophil values. These findings are crucial to understanding the high incidence in children; they also facilitate rapid understanding of the disease in clinical care settings and differentiate it from other febrile outbreaks. This will affect disease control, particularly in severe cases, and reduce mortality.

[18F]fluorodeoxyglucose positron emission tomography/computed tomography in combination with clinical data in predicting overall survival in non-small-cell lung cancer patients: A retrospective study

IntroductionPositron emission tomography/computed tomography (PET/CT) has an established role in evaluating patients with lung cancer. The aim of this work was to assess the predictive capability of [18F]Fluorodeoxyglucose ([18F]FDG) PET/CT parameters on overall survival (OS) in lung cancer patients using an artificial neural network (ANN) in parallel with conventional statistical analysis. MethodsRetrospective analysis was performed on a group of 165 lung cancer patients (98M, 67F). PET features associated with the primary tumor: maximum and mean standardized uptake value (SUVmax, SUVmean), total lesion glycolysis (TLG) metabolic tumor volume (MTV) and area under the curve-cumulative SUV histogram (AUC-CSH) and metastatic lesions (SUVmaxtotal, SUVmeantotal, TLGtotal, and MTVtotal) were evaluated. In parallel with conventional statistical analysis (Chi-Square analysis for nominal data, Student's t test for continuous data), the data was evaluated using an ANN. There were 97 input variables in 165 patients using a binary classification of either below, or greater than/equal to median survival post primary diagnosis. Additionally, phantom study was performed to assess the most optimal contouring method. ResultsMales had statistically higher SUVmax (mean: 10.7 vs 8.9; p = 0.020), MTV (mean: 66.5 cm3 vs. 21.5 cm3; p = 0.001), TLG (mean 404.7 vs. 115.0; p = 0.003), TLGtotal (mean: 946.7 vs. 433.3; p = 0.014) and MTVtotal (mean: 242.0 cm3 vs. 103.7 cm3; p = 0.027) than females. The ANN after training and validation was optimised with a final architecture of 4 scaling layer inputs (TLGtotal, SUVmaxtotal, SUVmeantotal and disease stage) and receiving operator characteristic (ROC) analysis demonstrated an AUC of 0.764 (sensitivity of 92.3%, specificity of 57.1%). ConclusionConventional statistical analysis and the ANN provided concordant findings in relation to variables that predict decreased survival. The ANN provided a weighted algorithm of the 4 key features to predict decreased survival. Implication for practiceIdentification of parameters which can predict survival in lung cancer patients might be helpful in choosing the group of patients who require closer look during the follow-up.

Evaluating Diffusion Models for the Automation of Ultrasonic Nondestructive Evaluation Data Analysis

We develop decision support and automation for the task of ultrasonic non-destructive evaluation data analysis. First, we develop a probabilistic model for the task and then implement the model as a series of neural networks based on Conditional Score-Based Diffusion and Denoising Diffusion Probabilistic Model architectures. We use the neural networks to generate estimates for peak amplitude response time of flight and perform a series of tests probing their behavior, capacity, and characteristics in terms of the probabilistic model. We train the neural networks on a series of datasets constructed from ultrasonic non-destructive evaluation data acquired during an inspection at a nuclear power generation facility. We modulate the partition classifying nominal and anomalous data in the dataset and observe that the probabilistic model predicts trends in neural network model performance, thereby demonstrating a principled basis for explainability. We improve on previous related work as our methods are self-supervised and require no data annotation or pre-processing, and we train on a per-dataset basis, meaning we do not rely on out-of-distribution generalization. The capacity of the probabilistic model to predict trends in neural network performance, as well as the quality of the estimates sampled from the neural networks, support the development of a technical justification for usage of the method in safety-critical contexts such as nuclear applications. The method may provide a basis or template for extension into similar non-destructive evaluation tasks in other industrial contexts.

Linear programming sensitivity measured by the optimal value worst-case analysis

This paper introduces the concept of a derivative of the optimal value function in linear programming (LP). Basically, it is the worst case optimal value of an interval LP problem when the nominal data are inflated to intervals according to given perturbation patterns. By definition, the derivative expresses how the optimal value can worsen when the data are subject to variations. In addition, it also gives a certain sensitivity measure or condition number of an LP problem. If the LP problem is nondegenerate, the derivatives are easy to calculate from the computed primal and dual optimal solutions. For degenerate problems, the computation is more difficult. We propose an upper bound and some kind of characterization, but there are many open problems remaining. We carried out numerical experiments with specific LP problems and with real LP data from Netlib repository. They show that the derivatives give a suitable sensitivity measure of LP problems. It remains an open problem how to efficiently and rigorously handle degenerate problems.

Lateral Epicondylitis: Treatment Preferences from the Potential Patient Perspective.

Knowing the questions and concerns that patients have regarding treatment options for lateral epicondylitis may allow for shared-decision making and potentially superior patient outcomes and satisfaction. In the present study, we aimed to further delineate patient preferences with treatment of lateral epicondylitis. An online, survey-based, descriptive study was conducted through Amazon Mechanical Turk. Survey participants were presented with a clinical scenario regarding lateral epicondylitis and asked four questions regarding treatment preferences for nonoperative treatment, whether they would consider platelet-rich plasma (PRP) injection, and whether they would consider surgical intervention for recalcitrant symptoms. A Likert scale was used for responses. McNemar chi-square test was used for paired nominal data for statistical analysis. A total of 238 survey responses were included. A majority (63%) of respondents elected to proceed with formal physical therapy. When given additional information regarding corticosteroid injections, 50.8% of respondents reported preferring physical therapy. There were no differences between groups for questions 1 and 2 (P = 0.90). Of the respondents, 75.2% were "likely" or "extremely likely" to consider PRP injection. When asked about surgical intervention, 74.8% of respondents were "likely" or "extremely likely" to proceed with continued symptoms. It is important to include patient preferences in treatment discussions of lateral epicondylitis. Survey respondents preferred formal physical therapy for initial treatment. A surprising majority of respondents were likely to consider a PRP injection. With prolonged symptoms, respondents were interested in discussions of surgical intervention and thus, it should continue to be offered to patients with recalcitrant symptoms.

Gender potentially affects early postoperative hyponatremia in pituitary adenoma: XGBoost-based predictive modeling

PurposeThe occurrence of hyponatremia is a prevalent complication following transnasal transsphenoidal surgery for pituitary adenoma surgery, which adversely affects patient prognosis, hospitalization duration, and rehospitalization risk. The primary objective of this study is to strengthen the correlation between clinical factors associated with pituitary adenoma and postoperative hyponatremia. Additionally, the study aims to develop a predictive model for postoperative hyponatremia in patients with pituitary adenoma, with the ultimate goal of establishing a basis for reducing the occurrence of postoperative hyponatremia following surgical interventions. MethodsThe chi-square test or Fisher test was employed for nominal data, while the t-test or Mann-Whitney test was utilized for continuous data analysis. In cases where the data exhibited statistical differences, binary logistic analysis was conducted to examine the risk and protective factors associated with postoperative hyponatremia. XGBoost was employed to construct predictive models for hyponatremia in this study. The patients were partitioned into training and test sets, and the most suitable parameters were determined through five-fold cross-validation and subsequently utilized for training on the training set. The discriminatory capability was assessed on the internal validation set. Results and conclusionsOut of the total 280 patients included in this investigation, 82 patients experienced early postoperative hyponatremia. Among these individuals, male gender (P = 0.02, odds ratio = 1.98) was identified as a risk factor for early postoperative hyponatremia, while preoperative chloride levels (P = 0.021, odds ratio = 0.866) and surgery time (P = 0.039, odds ratio = 0.990) were identified as protective factors against postoperative hyponatremia. The XGBoost model exhibited a sensitivity of 94.2%, a specificity of 61.5%, a positive predictive value of 51.6%, a negative predictive value of 96%, and identified male gender, preoperative sodium, and preoperative cortisol as the most significant predictors. Our findings indicate that gender may have influence in the development of early postoperative hyponatremia in patients with pituitary adenomas.

Working from home during and beyond the coronavirus pandemic: Employee reflections from the Czech Republic

Research background: Working from home (WFH) has become the norm for many people around the world due to the coronavirus pandemic. It has accelerated the implementation of WFH and has raised questions about its further use. Purpose of the article: The article analyzes the experience of employees in the Czech Republic with WFH during the coronavirus pandemic to identify perceived benefits, difficulties, skills, and conditions related to WFH, assess the employee desire to work from home depending on their industry, age, and gender, and propose actions for further use of WFH beyond the coronavirus pandemic. The statistical verification of the dependence of the desire of employees to work from home beyond the coronavirus pandemic on their industry, age, and gender is integral to the analysis. Methods: The analysis is founded on data from the authors' online questionnaire survey conducted from December 2021 to September 2022. Responses from 480 employees who experienced WFH during the coronavirus pandemic in the Czech Republic are analyzed. Statistical non-parametric methods for the analysis of nominal data are used to verify defined hypotheses. Findings & value added: A research gap concerning the further use of WFH in the Czech Republic and elsewhere by considering the employee experience is met. The findings have revealed the desire of more than half of surveyed employees to continue working from home part-time to achieve reasonable work-personal-family well-being. The desire turned out to be dependent on the employees' industry. Within further use, WFH should be taken as a specific work pattern for suitable professions and individuals with adequate organizational and technical support. Three matters concerning performance agreement, work environment, and self-management are proposed to enhance work-personal-family well-being and thus job satisfaction and productivity of homeworkers. The findings and proposals can help any theorist and practitioner interested in WFH.

Abstract 7373: Generative Bayesian networks for augmentation of molecular data from commercial genetics panels

Abstract Background: Computational models in medical research which use molecular features to predict patient sensitivities or outcomes are traditionally limited to producing a scalar output for a given disease phenotype, e.g. progression-free survival or drug response. Even for relatively intelligible models, it is generally difficult to place confidence limits on the prediction or develop an intuition for how a prediction varies with respect to “what-if” changes in those features or how sensitive a prediction is to changes in input variables. Moreover, while essential for understanding and modeling tumor behavior, molecular features from patient tumors in real-world settings are typically limited to profiles of hundreds of genes, while the biologically relevant inputs to the patient’s condition can be much larger. Approach: To generate data required to enhance the interpretation of predictive models and extend the utility of real-world patient oncology datasets, a distribution of conditions consistent with the observed features and their incidence across cancer patient cohorts is required. We hypothesized we could synthesize patient samples drawn from the joint probability distribution of a broader universe of features when a subset of them is held fixed at the observed values. We model this joint distribution by learning a Bayesian network over a broad feature set for which some training data is available and generate feature profiles by applying Gibbs sampling to the learned network. Results: We assessed the potential clinical utility of using these generated feature profiles in predicting drug response and enhancing biological interpretation of different model outputs. We found generative somatic mutations were useful for predicting cancer patient outcomes, including drug response predictions in breast cancer tumors from AACR Project GENIE. Synthesized patient feature profiles enhanced biological interpretability of nominal panel data (100-200 genes), providing a means to assign probabilities to outcomes and uncovering previously described mechanisms of drug response in real-world patient data. Conclusion: We introduce an approach that leverages Bayesian networks to synthesize richly annotated patient feature profiles from limited molecular data routinely collected in real-world settings. This approach addresses challenges associated with limited molecular data, biological interpretability and evaluation of predictive models and enhances the utility of real-world datasets for cancer research. Citation Format: Dillon H. Tracy, Jeff Sherman, Maayan Baron. Generative Bayesian networks for augmentation of molecular data from commercial genetics panels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7373.

Persistent COVID-19 parosmia and olfactory loss post olfactory training: randomized clinical trial comparing central and peripheral-acting therapeutics

PurposeAlthough COVID-19 anosmia is often transient, patients with persistent olfactory dysfunction (pOD) can experience refractory parosmia and diminished smell. This study evaluated four putative therapies for parosmia in patients with chronic COVID-19 olfactory impairment.MethodsAfter screening nasal endoscopy, 85 patients (49 female, 58%) with pOD and treatment-refractory parosmia were randomized to: (1) ultramicronized palmitoylethanolamide and luteolin + olfactory training (OT) (umPEALUT group, n = 17), (2) alpha-lipoic acid + OT (ALA group, n = 21), (3) umPEALUT + ALA + OT (combination group, n = 28), or 4) olfactory training (OT) alone (control group, n = 23). Olfactory function was assessed at baseline (T0) and 6 months (T1) using a parosmia questionnaire and Sniffin’ Sticks test of odor threshold, detection, and identification (TDI). Analyses included one-way ANOVA for numeric data and Chi-Square analyses for nominal data on parosmia.ResultsThe umPEALUT group had the largest improvement in TDI scores (21.8 ± 9.4 to 29.7 ± 7.5) followed by the combination group (19.6 ± 6.29 to 27.5 ± 2.7), both p < 0.01. The control and ALA groups had no significant change. Patients in the combination and umPEALUT groups had significantly improved TDI scores compared to ALA and control groups (p < 0.001). Rates of parosmia resolution after 6 months were reported at 96% for combination, 65% for control, 53% for umPEALUT and 29% for ALA (p < 0.001). All treatment regimens were well-tolerated.ConclusionsumPEALUT and OT, with or without ALA, was associated with improvement in TDI scores and parosmia, whereas OT alone or OT with ALA were associated with little benefit.

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.